Tandem metabolic reaction-based sensors unlock in vivo metabolomics.

Mimicking metabolic pathways on electrodes enables in vivo metabolite monitoring for decoding metabolism. Conventional in vivo sensors cannot accommodate underlying complex reactions involving multiple enzymes and cofactors, addressing only a fraction of enzymatic reactions for few metabolites. We devised a single-wall-carbon-nanotube-electrode architecture supporting tandem metabolic pathway-like reactions linkable to oxidoreductase-based electrochemical analysis, making a vast majority of metabolites detectable in vivo.

Rapidly self-healing electronic skin for machine learning-assisted physiological and movement evaluation.

Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their functionality, underscoring urgent need for self-healing E-Skins resistant to mechanical damage. Current materials have slow recovery times, impeding reliable signal measurement.

Mechanistic and data-driven modeling of carbon respiration with bio-electrochemical sensors.

Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs.

Integrating Bio-Electrochemical Sensors and Machine Learning to Predict the Efficacy of Biological Nutrient Removal Processes at Water Resource Recovery Facilities.

Monitoring biological nutrient removal (BNR) processes at water resource recovery facilities (WRRFs) with data-driven models is currently limited by the data limitations associated with the variability of bioavailable carbon (C) in wastewater. This study focuses on leveraging the amperometric response of a bio-electrochemical sensor (BES) to wastewater C variability, to predict influent shock loading events and NO removal in the first-stage anoxic zone (ANX1) of a five-stage Bardenpho BNR process using machine learning (ML) methods. Shock loading prediction with BES signal processing successfully detected 86.

Integrated Agricultural Practices and Engineering Technologies Enhance Synergies of Food-Energy-Water Systems in Corn Belt Watersheds.

Interconnected food, energy, water systems (FEWS) require systems level understanding to design efficient and effective management strategies and policies that address potentially competing challenges of production and environmental quality. Adoption of agricultural best management practices (BMPs) can reduce nonpoint source phosphorus (P) loads, but there are also opportunities to recover P from point sources, which could also reduce demand for mineral P fertilizer derived from declining geologic reserves. Here, we apply the Integrated Technology-Environment-Economics Model to investigate the consequences of watershed-scale portfolios of agricultural BMPs and environmental and biological technologies (EBTs) for co-benefits of FEWS in Corn Belt watersheds.